Final Project: Travel Greener

Authors: Haoran Zhang, Qi(David) Lin, Xiyu Chen, Yao Yao

Busy Traffic in New York City

Background

In today’s world, transportation is an integral part of us. However, current transportation systems are accompanied by a variety of issues, including global warming, environmental degradation, health effects, and greenhouse gas emissions. In fact, the transport sector will account for 23% of global greenhouse gas emissions from burning fossil fuels. Road transport accounts for the largest share of total greenhouse gas emissions at 75%, and this trend is expected to increase in the future.

Green transportation is critical because it can reduce the burden on the natural resources of fossil fuels such as oil, natural gas, and petroleum on the planet. Another reason why green transportation is essential is that the use of fossil fuels poses a considerable threat to the environment and humanity itself. When the smoke from a moving vehicle is inhaled by humans, it increases the risk of people suffering from diseases such as asthma. Green transportation can alleviate this problem. Also, green transit can save a lot of money we spend on gasoline.

Simulation Ideas

Increasing Oil Prices

In the final project, we simulate how policy changes (1. oil tax, 2. Citi bike membership discount, 3. bus monthly card, 4.subway price) will influence the use of transportation modes (car, bike, bus, and subway) of citizens in a specific area. This simulation can be used as a helpful tool to help policymakers better predict the changes in people’s travel patterns after changing one or more transportation policies. Policymakers can use this tool to develop sound policies to enable people in this region to travel more efficiently and encourage people to use more environmentally friendly travel methods.

Parameters (Input):

Different Transportation Modes in NYC

• Changes in oil tax
• Citi Bike membership discount
• Bus monthly card discount
• Subway discount

Result (Output):

The number of people using bicycles, buses, subways, and cars (different transportation mode)

Diagram

Diagram Methodology, Drawn by the Author

Simulation examples

The following is one of the simulation examples of how we use our tool to predict the results.

• In the first group, firstly, we set the discount of bike membership, bus monthly card and subway ticket at the lowest level (0%), and set the oil tax at 5% to see the simulation result.
• Then we change the oil tax from 5% to 20% to see the change of transportation users.

Processing in Unity

Method

Literature Review

Investigate the impact of different transportation policies on the use of transportation modes.

Multiple linear regression

For each transportation mode, we conduct multiple linear regression to get their regression formula. To better achieve the reality of simulation, we add random number function in Unity. The formulas are as follows:

Bike = 1.08a+3.21b-1.32c-1.90d+e1+σ

Bus = 1.33a-1.31b+3.51c-1.02d+e2+σ

Subway = 1.09a-1.82b-1.50c+4.02d+e3+σ

Car = -3.34a -1.52b-1.87c-1.49d+e4+σ

a: oil tax [1%, 15%]

b: bicycle membership discount [1%, 20%]

c: monthly bus card discount [1%, 20%]

d: subway discount [1%, 20%]

e1-e4: constant

σ: random number

Illustration of the multiple linear regression process

Visualization in Unity

We visualize our ideas in Unity. There are four “destination”: car, bike, bus, and subway. People are produced based on our regression model. Every time people arrive at the destination, we add one number.

Evaluation of our tools

Advantage:

By using this tool, policymakers can easily accurately predict the changes in people’s travel modes after changing one or more transportation policies.

Disadvantage:

The simulation system is based on the New York City transportation system. New York City has a relatively developed public transportation system. It may not be suitable for other cities.

Reference

Fernández L., J. E., de Cea Ch., J., & de Grange C., L. (2005). Production costs, congestion, scope and scale economies in urban bus transportation corridors. Transportation Research Part A: Policy and Practice, 39(5), 383–403. https://doi.org/10.1016/j.tra.2004.08.003

Frankena, M. W. (1981). The effects of alternative urban transit subsidy formulas. Journal of Public Economics, 15(3), 337–348. https://doi.org/10.1016/0047-2727(81)90015-3

Gaudry, M. J. I. (1980). A study of aggregate bi-modal urban travel supply, demand and network behavior using simultaneous equations with autoregressive residuals. Transportation Research Part B: Methodological, 14(1–2), 29–58. https://doi.org/10.1016/0191-2615(80)90031-4

Glaister, S., & Lewis, D. (1978). An integrated fares policy for transport in London. Journal of Public Economics, 9(3), 341–355. https://doi.org/10.1016/0047-2727(78)90015-4

MTA. (2018). Ridership Trends.

Saggar, R. K. (2009). Economies of scale in bus transport: An indian case study*: bus transport in india. Bulletin of the Oxford University Institute of Economics & Statistics, 30(4), 341–346. https://doi.org/10.1111/j.1468-0084.1968.mp30004005.x

Modes and benefits of green transportation. (2013, April 29). Retrieved December 16, 2019, from Conserve Energy Future website: https://www.conserve-energy-future.com/name=modes-and-benefits-of-green-transportation

Green transportation. (2017, October 25). Retrieved December 16, 2019, from Ierek news website: https://www.ierek.com/news/index.php/2017/10/25/green-transportation/

Final energy consumption in Europe by mode of transport [Indicator Assessment]. (n.d.). Retrieved December 16, 2019, from European Environment Agency website: https://www.eea.europa.eu/data-and-maps/indicators/transport-final-energy-consumption-by-mode/assessment-9

// UI/UX 
// Slider
// SliderValueToText
// 
using UnityEngine;
using System.Collections;
using UnityEngine.UI;
public class SliderValueToText : MonoBehaviour
{
public Slider sliderUI;
private Text textSliderValue;
void Start()
{
textSliderValue = GetComponent<Text>();
ShowSliderValue();
}
public void ShowSliderValue()
{
string sliderMessage = sliderUI.value + “%”;
textSliderValue.text = sliderMessage;
}
}
------------------------------------------------------------------
// MonoBehaviour
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.UI;
public class bike_receiver : MonoBehaviour
{
// Start is called before the first frame update
public Text countText;
private Rigidbody rb;
private int count;
void Start()
{
rb = GetComponent<Rigidbody>();
count = 0;
SetCountText();
}
//void FixedUpdate()
//{
//}
void OnTriggerEnter(Collider other)
{
if (other.gameObject.CompareTag("Pick Up"))
{
other.gameObject.SetActive(false);
count = count + 1;
SetCountText();
}
}
void SetCountText()
{
countText.text = "Bike User Number Count:" + count.ToString();
}
}
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class bike : MonoBehaviour
{
public GameObject wall;
// Start is called before the first frame update
void Start()
{
}
// Update is called once per frame
void Update()
{
float speed = 10;
wall.transform.localPosition = Vector3.MoveTowards(gameObject.transform.localPosition, new Vector3(-7, 0, 30), speed * Time.deltaTime);
}
}
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.UI;
public class PlayerController : MonoBehaviour
{
// Start is called before the first frame update
public Text countText;
private Rigidbody rb;
private int count;
void Start()
{
rb = GetComponent<Rigidbody>();
count = 0;
SetCountText();
}
//void FixedUpdate()
//{
//}
void OnTriggerEnter(Collider other)
{
if (other.gameObject.CompareTag("Pick Up"))
{
other.gameObject.SetActive(false);
count = count + 1;
SetCountText();
}
}
void SetCountText()
{
countText.text = "Count:" + count.ToString();
}
}